Performed data-scraping and data-cleaning to create a dataset of Consumer Price Index for Delhi in various category of goods and Services, which is to be utilized in machine learning model to analyse and predict next 19 months.

burgahDecomposed the time series data using Additive Seasonal Decomposition into the following components: Trend, Seasonality & Residue. Conducted Augmented-Dickey Fuller test to check for stationarity. Used first-order differencing to convert non-stationary time series into stationary.

Built a SARIMA model using seasonal_order = (2, 0, 1, 12) to predict the consimer price index for food and beverages goods in Delhi for 2023. Forecast revealed CPI crossing the 400.00 mark which directly disrupts the F&B companies. Data analysis for predicted values suggest that there has been an astonishing 85% increase in growth rate for CPI of F&B goods in last 10 years with an annual growth rate of 6%